Gym-MiniGrid/pytorch_rl/main.py

import copy
import glob
import os
import time
import operator
from functools import reduce

import gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable

from arguments import get_args
from vec_env.dummy_vec_env import DummyVecEnv
from vec_env.subproc_vec_env import SubprocVecEnv
from envs import make_env
from kfac import KFACOptimizer
from model import Policy
from storage import RolloutStorage
from visualize import visdom_plot

args = get_args()

assert args.algo in ['a2c', 'ppo', 'acktr']
if args.recurrent_policy:
    assert args.algo in ['a2c', 'ppo'], 'Recurrent policy is not implemented for ACKTR'

num_updates = int(args.num_frames) // args.num_steps // args.num_processes

torch.manual_seed(args.seed)
if args.cuda:
    torch.cuda.manual_seed(args.seed)

try:
    os.makedirs(args.log_dir)
except OSError:
    files = glob.glob(os.path.join(args.log_dir, '*.monitor.csv'))
    for f in files:
        os.remove(f)

def main():
    os.environ['OMP_NUM_THREADS'] = '1'

    envs = [make_env(args.env_name, args.seed, i, args.log_dir) for i in range(args.num_processes)]

    if args.num_processes > 1:
        envs = SubprocVecEnv(envs)
    else:
        envs = DummyVecEnv(envs)

    obs_shape = envs.observation_space.shape
    obs_shape = (obs_shape[0] * args.num_stack, *obs_shape[1:])
    obs_numel = reduce(operator.mul, obs_shape, 1)

    actor_critic = Policy(obs_numel, envs.action_space)

    # Maxime: log some info about the model and its size
    modelSize = 0
    for p in actor_critic.parameters():
        pSize = reduce(operator.mul, p.size(), 1)
        modelSize += pSize
    print(str(actor_critic))
    print('Total model size: %d' % modelSize)

    if envs.action_space.__class__.__name__ == "Discrete":
        action_shape = 1
    else:
        action_shape = envs.action_space.shape[0]

    if args.cuda:
        actor_critic.cuda()

    if args.algo == 'a2c':
        optimizer = optim.RMSprop(actor_critic.parameters(), args.lr, eps=args.eps, alpha=args.alpha)
    elif args.algo == 'ppo':
        optimizer = optim.Adam(actor_critic.parameters(), args.lr, eps=args.eps)
    elif args.algo == 'acktr':
        optimizer = KFACOptimizer(actor_critic)

    rollouts = RolloutStorage(args.num_steps, args.num_processes, obs_shape, envs.action_space, actor_critic.state_size)
    current_obs = torch.zeros(args.num_processes, *obs_shape)

    def update_current_obs(obs):
        shape_dim0 = envs.observation_space.shape[0]
        obs = torch.from_numpy(obs).float()
        if args.num_stack > 1:
            current_obs[:, :-shape_dim0] = current_obs[:, shape_dim0:]
        current_obs[:, -shape_dim0:] = obs

    obs = envs.reset()
    update_current_obs(obs)
    rollouts.observations[0].copy_(current_obs)

    # These variables are used to compute average rewards for all processes.
    episode_rewards = torch.zeros([args.num_processes, 1])
    final_rewards = torch.zeros([args.num_processes, 1])

    if args.cuda:
        current_obs = current_obs.cuda()
        rollouts.cuda()

    start = time.time()
    for j in range(num_updates):
        for step in range(args.num_steps):
            # Sample actions
            value, action, action_log_prob, states = actor_critic.act(
                Variable(rollouts.observations[step], volatile=True),
                Variable(rollouts.states[step], volatile=True),
                Variable(rollouts.masks[step], volatile=True)
            )
            cpu_actions = action.data.squeeze(1).cpu().numpy()

            # Obser reward and next obs
            obs, reward, done, info = envs.step(cpu_actions)
            reward = torch.from_numpy(np.expand_dims(np.stack(reward), 1)).float()
            episode_rewards += reward

            # If done then clean the history of observations.
            masks = torch.FloatTensor([[0.0] if done_ else [1.0] for done_ in done])
            final_rewards *= masks
            final_rewards += (1 - masks) * episode_rewards
            episode_rewards *= masks

            if args.cuda:
                masks = masks.cuda()

            if current_obs.dim() == 4:
                current_obs *= masks.unsqueeze(2).unsqueeze(2)
            elif current_obs.dim() == 3:
                current_obs *= masks.unsqueeze(2)
            else:
                current_obs *= masks

            update_current_obs(obs)
            rollouts.insert(step, current_obs, states.data, action.data, action_log_prob.data, value.data, reward, masks)

        next_value = actor_critic(
            Variable(rollouts.observations[-1], volatile=True),
            Variable(rollouts.states[-1], volatile=True),
            Variable(rollouts.masks[-1], volatile=True)
        )[0].data

        rollouts.compute_returns(next_value, args.use_gae, args.gamma, args.tau)

        if args.algo in ['a2c', 'acktr']:
            values, action_log_probs, dist_entropy, states = actor_critic.evaluate_actions(
                Variable(rollouts.observations[:-1].view(-1, *obs_shape)),
                Variable(rollouts.states[:-1].view(-1, actor_critic.state_size)),
                Variable(rollouts.masks[:-1].view(-1, 1)),
                Variable(rollouts.actions.view(-1, action_shape))
            )

            values = values.view(args.num_steps, args.num_processes, 1)
            action_log_probs = action_log_probs.view(args.num_steps, args.num_processes, 1)

            advantages = Variable(rollouts.returns[:-1]) - values
            value_loss = advantages.pow(2).mean()

            action_loss = -(Variable(advantages.data) * action_log_probs).mean()

            if args.algo == 'acktr' and optimizer.steps % optimizer.Ts == 0:
                # Sampled fisher, see Martens 2014
                actor_critic.zero_grad()
                pg_fisher_loss = -action_log_probs.mean()

                value_noise = Variable(torch.randn(values.size()))
                if args.cuda:
                    value_noise = value_noise.cuda()

                sample_values = values + value_noise
                vf_fisher_loss = -(values - Variable(sample_values.data)).pow(2).mean()

                fisher_loss = pg_fisher_loss + vf_fisher_loss
                optimizer.acc_stats = True
                fisher_loss.backward(retain_graph=True)
                optimizer.acc_stats = False

            optimizer.zero_grad()
            (value_loss * args.value_loss_coef + action_loss - dist_entropy * args.entropy_coef).backward()

            if args.algo == 'a2c':
                nn.utils.clip_grad_norm(actor_critic.parameters(), args.max_grad_norm)

            optimizer.step()
        elif args.algo == 'ppo':
            advantages = rollouts.returns[:-1] - rollouts.value_preds[:-1]
            advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-5)

            for e in range(args.ppo_epoch):
                if args.recurrent_policy:
                    data_generator = rollouts.recurrent_generator(advantages, args.num_mini_batch)
                else:
                    data_generator = rollouts.feed_forward_generator(advantages, args.num_mini_batch)

                for sample in data_generator:
                    observations_batch, states_batch, actions_batch, \
                       return_batch, masks_batch, old_action_log_probs_batch, \
                            adv_targ = sample

                    # Reshape to do in a single forward pass for all steps
                    values, action_log_probs, dist_entropy, states = actor_critic.evaluate_actions(
                        Variable(observations_batch),
                        Variable(states_batch),
                        Variable(masks_batch),
                        Variable(actions_batch)
                    )

                    adv_targ = Variable(adv_targ)
                    ratio = torch.exp(action_log_probs - Variable(old_action_log_probs_batch))
                    surr1 = ratio * adv_targ
                    surr2 = torch.clamp(ratio, 1.0 - args.clip_param, 1.0 + args.clip_param) * adv_targ
                    action_loss = -torch.min(surr1, surr2).mean() # PPO's pessimistic surrogate (L^CLIP)

                    value_loss = (Variable(return_batch) - values).pow(2).mean()

                    optimizer.zero_grad()
                    (value_loss + action_loss - dist_entropy * args.entropy_coef).backward()
                    nn.utils.clip_grad_norm(actor_critic.parameters(), args.max_grad_norm)
                    optimizer.step()

        rollouts.after_update()

        if j % args.save_interval == 0 and args.save_dir != "":
            save_path = os.path.join(args.save_dir, args.algo)
            try:
                os.makedirs(save_path)
            except OSError:
                pass

            # A really ugly way to save a model to CPU
            save_model = actor_critic
            if args.cuda:
                save_model = copy.deepcopy(actor_critic).cpu()

            save_model = [save_model,
                            hasattr(envs, 'ob_rms') and envs.ob_rms or None]

            torch.save(save_model, os.path.join(save_path, args.env_name + ".pt"))

        if j % args.log_interval == 0:
            end = time.time()
            total_num_steps = (j + 1) * args.num_processes * args.num_steps
            print(
                "Updates {}, num timesteps {}, FPS {}, mean/median reward {:.1f}/{:.1f}, min/max reward {:.1f}/{:.1f}, entropy {:.5f}, value loss {:.5f}, policy loss {:.5f}".
                format(
                    j,
                    total_num_steps,
                    int(total_num_steps / (end - start)),
                    final_rewards.mean(),
                    final_rewards.median(),
                    final_rewards.min(),
                    final_rewards.max(), dist_entropy.data[0],
                    value_loss.data[0], action_loss.data[0]
                )
            )

        if args.vis and j % args.vis_interval == 0:
            win = visdom_plot(
                total_num_steps,
                final_rewards.mean()
            )

if __name__ == "__main__":
    main()